Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations1471
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory402.4 KiB
Average record size in memory280.1 B

Variable types

Numeric15
Boolean3
Categorical17

Alerts

EmployeeCount has constant value "1" Constant
Over18 has constant value "True" Constant
StandardHours has constant value "80" Constant
Dataset has 1 (0.1%) duplicate rowsDuplicates
Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
JobLevel is highly overall correlated with JobRole and 2 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
NumCompaniesWorked has 197 (13.4%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2025-01-14 07:17:39.727694
Analysis finished2025-01-14 07:18:38.899201
Duration59.17 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.917631
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:39.062436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1366791
Coefficient of variation (CV)0.24748823
Kurtosis-0.40291747
Mean36.917631
Median Absolute Deviation (MAD)6
Skewness0.41476068
Sum54232
Variance83.478906
MonotonicityNot monotonic
2025-01-14T12:18:39.221306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 67
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size3.0 KiB
False
1232 
True
237 
(Missing)
 
2
ValueCountFrequency (%)
False 1232
83.8%
True 237
 
16.1%
(Missing) 2
 
0.1%
2025-01-14T12:18:39.348389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Travel_Rarely
1044 
Travel_Frequently
277 
Non-Travel
150 

Length

Max length17
Median length13
Mean length13.447315
Min length10

Characters and Unicode

Total characters19781
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 1044
71.0%
Travel_Frequently 277
 
18.8%
Non-Travel 150
 
10.2%

Length

2025-01-14T12:18:39.523853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:39.777443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 1044
71.0%
travel_frequently 277
 
18.8%
non-travel 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 3069
15.5%
r 2792
14.1%
l 2792
14.1%
a 2515
12.7%
T 1471
7.4%
v 1471
7.4%
y 1321
6.7%
_ 1321
6.7%
R 1044
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3069
15.5%
r 2792
14.1%
l 2792
14.1%
a 2515
12.7%
T 1471
7.4%
v 1471
7.4%
y 1321
6.7%
_ 1321
6.7%
R 1044
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3069
15.5%
r 2792
14.1%
l 2792
14.1%
a 2515
12.7%
T 1471
7.4%
v 1471
7.4%
y 1321
6.7%
_ 1321
6.7%
R 1044
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3069
15.5%
r 2792
14.1%
l 2792
14.1%
a 2515
12.7%
T 1471
7.4%
v 1471
7.4%
y 1321
6.7%
_ 1321
6.7%
R 1044
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.72672
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:39.999214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.5
Q1465
median802
Q31157
95-th percentile1424
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.47772
Coefficient of variation (CV)0.50263398
Kurtosis-1.2040728
Mean802.72672
Median Absolute Deviation (MAD)344
Skewness-0.0048492901
Sum1180811
Variance162794.27
MonotonicityNot monotonic
2025-01-14T12:18:40.252965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
408 5
 
0.3%
530 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
1157 5
 
0.3%
829 4
 
0.3%
1469 4
 
0.3%
267 4
 
0.3%
Other values (876) 1423
96.7%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Department
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size11.6 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.542177
Min length5

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 961
65.3%
Sales 446
30.3%
Human Resources 63
 
4.3%
(Missing) 1
 
0.1%

Length

2025-01-14T12:18:40.514713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:40.668293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
research 961
27.8%
961
27.8%
development 961
27.8%
sales 446
12.9%
human 63
 
1.8%
resources 63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9.177551
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:40.825093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.0998138
Coefficient of variation (CV)0.8825681
Kurtosis-0.21524079
Mean9.177551
Median Absolute Deviation (MAD)5
Skewness0.96153081
Sum13491
Variance65.606984
MonotonicityNot monotonic
2025-01-14T12:18:41.031153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 212
14.4%
1 208
14.1%
10 86
 
5.8%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 443
30.1%
ValueCountFrequency (%)
1 208
14.1%
2 212
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.8%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 27
1.8%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
399 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 399
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2025-01-14T12:18:41.268564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:41.506650image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 399
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 399
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 399
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 399
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 399
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

EducationField
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size11.6 KiB
Life Sciences
605 
Medical
465 
Marketing
159 
Technical Degree
132 
Other
82 

Length

Max length16
Median length15
Mean length10.529252
Min length5

Characters and Unicode

Total characters15478
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 605
41.1%
Medical 465
31.6%
Marketing 159
 
10.8%
Technical Degree 132
 
9.0%
Other 82
 
5.6%
Human Resources 27
 
1.8%
(Missing) 1
 
0.1%

Length

2025-01-14T12:18:41.744797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:41.998374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
life 605
27.1%
sciences 605
27.1%
medical 465
20.8%
marketing 159
 
7.1%
technical 132
 
5.9%
degree 132
 
5.9%
other 82
 
3.7%
human 27
 
1.2%
resources 27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 3103
20.0%
i 1966
12.7%
c 1966
12.7%
n 923
 
6.0%
a 783
 
5.1%
764
 
4.9%
s 659
 
4.3%
M 624
 
4.0%
L 605
 
3.9%
f 605
 
3.9%
Other values (16) 3480
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3103
20.0%
i 1966
12.7%
c 1966
12.7%
n 923
 
6.0%
a 783
 
5.1%
764
 
4.9%
s 659
 
4.3%
M 624
 
4.0%
L 605
 
3.9%
f 605
 
3.9%
Other values (16) 3480
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3103
20.0%
i 1966
12.7%
c 1966
12.7%
n 923
 
6.0%
a 783
 
5.1%
764
 
4.9%
s 659
 
4.3%
M 624
 
4.0%
L 605
 
3.9%
f 605
 
3.9%
Other values (16) 3480
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3103
20.0%
i 1966
12.7%
c 1966
12.7%
n 923
 
6.0%
a 783
 
5.1%
764
 
4.9%
s 659
 
4.3%
M 624
 
4.0%
L 605
 
3.9%
f 605
 
3.9%
Other values (16) 3480
22.5%

EmployeeCount
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1471 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1471
100.0%

Length

2025-01-14T12:18:42.426816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:42.570899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1471
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1471
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1471
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1471
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1471
100.0%

EmployeeNumber
Real number (ℝ)

Distinct1470
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.4677
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:42.680123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.5
Q1489.5
median1019
Q31555.5
95-th percentile1967.5
Maximum2068
Range2067
Interquartile range (IQR)1066

Descriptive statistics

Standard deviation602.0127
Coefficient of variation (CV)0.58763462
Kurtosis-1.2236014
Mean1024.4677
Median Absolute Deviation (MAD)534
Skewness0.017923095
Sum1506992
Variance362419.29
MonotonicityNot monotonic
2025-01-14T12:18:42.921626image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
440 2
 
0.1%
1374 1
 
0.1%
1390 1
 
0.1%
1389 1
 
0.1%
1387 1
 
0.1%
1383 1
 
0.1%
1382 1
 
0.1%
1380 1
 
0.1%
1379 1
 
0.1%
1377 1
 
0.1%
Other values (1460) 1460
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
2068 1
0.1%
2065 1
0.1%
2064 1
0.1%
2062 1
0.1%
2061 1
0.1%
2060 1
0.1%
2057 1
0.1%
2056 1
0.1%
2055 1
0.1%
2054 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
285 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 285
19.4%

Length

2025-01-14T12:18:43.050507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:43.175077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 285
19.4%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 285
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 285
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 285
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 285
19.4%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Male
883 
Female
588 

Length

Max length6
Median length4
Mean length4.7994562
Min length4

Characters and Unicode

Total characters7060
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 883
60.0%
Female 588
40.0%

Length

2025-01-14T12:18:43.301345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:43.438689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
male 883
60.0%
female 588
40.0%

Most occurring characters

ValueCountFrequency (%)
e 2059
29.2%
a 1471
20.8%
l 1471
20.8%
M 883
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2059
29.2%
a 1471
20.8%
l 1471
20.8%
M 883
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2059
29.2%
a 1471
20.8%
l 1471
20.8%
M 883
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2059
29.2%
a 1471
20.8%
l 1471
20.8%
M 883
12.5%
F 588
 
8.3%
m 588
 
8.3%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.903467
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:43.571933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.327996
Coefficient of variation (CV)0.30845108
Kurtosis-1.1966112
Mean65.903467
Median Absolute Deviation (MAD)18
Skewness-0.033633457
Sum96944
Variance413.22741
MonotonicityNot monotonic
2025-01-14T12:18:43.722234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
84 29
 
2.0%
98 28
 
1.9%
42 28
 
1.9%
48 28
 
1.9%
57 27
 
1.8%
79 27
 
1.8%
96 27
 
1.8%
54 26
 
1.8%
52 26
 
1.8%
Other values (61) 1196
81.3%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
84

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 84
 
5.7%

Length

2025-01-14T12:18:43.960835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:44.198417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 84
 
5.7%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 84
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 84
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 84
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 84
 
5.7%

JobLevel
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
544 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 544
37.0%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2025-01-14T12:18:44.419796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:44.657752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 544
37.0%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 544
37.0%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 544
37.0%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 544
37.0%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 544
37.0%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

JobRole
Categorical

High correlation 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Sales Executive
326 
Research Scientist
293 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length21
Mean length18.0707
Min length7

Characters and Unicode

Total characters26582
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive 326
22.2%
Research Scientist 293
19.9%
Laboratory Technician 259
17.6%
Manufacturing Director 145
9.9%
Healthcare Representative 131
8.9%
Manager 102
 
6.9%
Sales Representative 83
 
5.6%
Research Director 80
 
5.4%
Human Resources 52
 
3.5%

Length

2025-01-14T12:18:44.895918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:45.197031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
sales 409
14.4%
research 373
13.1%
executive 326
11.5%
scientist 293
10.3%
laboratory 259
9.1%
technician 259
9.1%
director 225
7.9%
representative 214
7.5%
manufacturing 145
 
5.1%
healthcare 131
 
4.6%
Other values (3) 206
7.3%

Most occurring characters

ValueCountFrequency (%)
e 3908
14.7%
a 2581
 
9.7%
t 2100
 
7.9%
c 2063
 
7.8%
i 2014
 
7.6%
r 1985
 
7.5%
n 1469
 
5.5%
s 1393
 
5.2%
1369
 
5.2%
o 795
 
3.0%
Other values (19) 6905
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3908
14.7%
a 2581
 
9.7%
t 2100
 
7.9%
c 2063
 
7.8%
i 2014
 
7.6%
r 1985
 
7.5%
n 1469
 
5.5%
s 1393
 
5.2%
1369
 
5.2%
o 795
 
3.0%
Other values (19) 6905
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3908
14.7%
a 2581
 
9.7%
t 2100
 
7.9%
c 2063
 
7.8%
i 2014
 
7.6%
r 1985
 
7.5%
n 1469
 
5.5%
s 1393
 
5.2%
1369
 
5.2%
o 795
 
3.0%
Other values (19) 6905
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3908
14.7%
a 2581
 
9.7%
t 2100
 
7.9%
c 2063
 
7.8%
i 2014
 
7.6%
r 1985
 
7.5%
n 1469
 
5.5%
s 1393
 
5.2%
1369
 
5.2%
o 795
 
3.0%
Other values (19) 6905
26.0%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
460 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 460
31.3%
3 442
30.0%
1 289
19.6%
2 280
19.0%

Length

2025-01-14T12:18:45.481243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:45.729498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 460
31.3%
3 442
30.0%
1 289
19.6%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 460
31.3%
3 442
30.0%
1 289
19.6%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 460
31.3%
3 442
30.0%
1 289
19.6%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 460
31.3%
3 442
30.0%
1 289
19.6%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 460
31.3%
3 442
30.0%
1 289
19.6%
2 280
19.0%

MaritalStatus
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Married
674 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.9027872
Min length6

Characters and Unicode

Total characters10154
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 674
45.8%
Single 470
32.0%
Divorced 327
22.2%

Length

2025-01-14T12:18:45.956317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:46.218390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
married 674
45.8%
single 470
32.0%
divorced 327
22.2%

Most occurring characters

ValueCountFrequency (%)
r 1675
16.5%
i 1471
14.5%
e 1471
14.5%
d 1001
9.9%
M 674
6.6%
a 674
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1675
16.5%
i 1471
14.5%
e 1471
14.5%
d 1001
9.9%
M 674
6.6%
a 674
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1675
16.5%
i 1471
14.5%
e 1471
14.5%
d 1001
9.9%
M 674
6.6%
a 674
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1675
16.5%
i 1471
14.5%
e 1471
14.5%
d 1001
9.9%
M 674
6.6%
a 674
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

MonthlyIncome
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6500.8654
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:46.442575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2098
Q12911
median4908
Q38378
95-th percentile17817.5
Maximum19999
Range18990
Interquartile range (IQR)5467

Descriptive statistics

Standard deviation4707.0221
Coefficient of variation (CV)0.72406085
Kurtosis1.0082126
Mean6500.8654
Median Absolute Deviation (MAD)2188
Skewness1.3708344
Sum9562773
Variance22156057
MonotonicityNot monotonic
2025-01-14T12:18:46.601960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
2404 3
 
0.2%
6142 3
 
0.2%
2559 3
 
0.2%
2741 3
 
0.2%
5562 3
 
0.2%
2610 3
 
0.2%
2380 3
 
0.2%
2451 3
 
0.2%
3452 3
 
0.2%
Other values (1339) 1440
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

MonthlyRate
Real number (ℝ)

Distinct1427
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14320.19
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:46.740449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3385.5
Q18049
median14242
Q320464.5
95-th percentile25431
Maximum26999
Range24905
Interquartile range (IQR)12415.5

Descriptive statistics

Standard deviation7120.5534
Coefficient of variation (CV)0.49723876
Kurtosis-1.2158959
Mean14320.19
Median Absolute Deviation (MAD)6203
Skewness0.017690985
Sum21064999
Variance50702280
MonotonicityNot monotonic
2025-01-14T12:18:46.878482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 3
 
0.2%
9150 3
 
0.2%
2755 2
 
0.1%
13008 2
 
0.1%
21534 2
 
0.1%
20364 2
 
0.1%
5355 2
 
0.1%
22074 2
 
0.1%
21981 2
 
0.1%
10494 2
 
0.1%
Other values (1417) 1449
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6947655
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:47.005323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4978834
Coefficient of variation (CV)0.92693908
Kurtosis0.0066784475
Mean2.6947655
Median Absolute Deviation (MAD)1
Skewness1.0245772
Sum3964
Variance6.2394216
MonotonicityNot monotonic
2025-01-14T12:18:47.107409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.4%
7 74
 
5.0%
6 70
 
4.8%
5 64
 
4.4%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.4%
5 64
 
4.4%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 64
 
4.4%
4 139
 
9.4%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

Over18
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1471 
ValueCountFrequency (%)
True 1471
100.0%
2025-01-14T12:18:47.226501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
417 
ValueCountFrequency (%)
False 1054
71.7%
True 417
 
28.3%
2025-01-14T12:18:47.367607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.208022
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:47.541462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6591462
Coefficient of variation (CV)0.24060632
Kurtosis-0.29865832
Mean15.208022
Median Absolute Deviation (MAD)2
Skewness0.82218445
Sum22371
Variance13.389351
MonotonicityNot monotonic
2025-01-14T12:18:47.733675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 210
14.3%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 210
14.3%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%

PerformanceRating
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1245 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1245
84.6%
4 226
 
15.4%

Length

2025-01-14T12:18:47.951335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:48.080288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1245
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1245
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1245
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1245
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1245
84.6%
4 226
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
433 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 433
29.4%
2 303
20.6%
1 276
18.8%

Length

2025-01-14T12:18:48.363185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:48.496880image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 433
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 433
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 433
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 433
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 433
29.4%
2 303
20.6%
1 276
18.8%

StandardHours
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
80
1471 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2942
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
80 1471
100.0%

Length

2025-01-14T12:18:48.621470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:48.751039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
80 1471
100.0%

Most occurring characters

ValueCountFrequency (%)
8 1471
50.0%
0 1471
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 1471
50.0%
0 1471
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 1471
50.0%
0 1471
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 1471
50.0%
0 1471
50.0%

StockOptionLevel
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
632 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 632
43.0%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2025-01-14T12:18:48.901412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:49.036525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 632
43.0%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 632
43.0%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 632
43.0%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 632
43.0%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 632
43.0%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

TotalWorkingYears
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.275323
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:49.154311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7798577
Coefficient of variation (CV)0.6899898
Kurtosis0.92020533
Mean11.275323
Median Absolute Deviation (MAD)4
Skewness1.1180988
Sum16586
Variance60.526187
MonotonicityNot monotonic
2025-01-14T12:18:49.376132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 89
 
6.1%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 89
6.1%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.800136
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:49.597645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892122
Coefficient of variation (CV)0.46041056
Kurtosis0.49235138
Mean2.800136
Median Absolute Deviation (MAD)1
Skewness0.55147089
Sum4119
Variance1.662068
MonotonicityNot monotonic
2025-01-14T12:18:49.787575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 124
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 124
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 124
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
345 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1471
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 345
 
23.5%
4 153
 
10.4%
1 80
 
5.4%

Length

2025-01-14T12:18:50.009392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T12:18:50.262633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 345
 
23.5%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 345
 
23.5%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 345
 
23.5%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 345
 
23.5%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 345
 
23.5%
4 153
 
10.4%
1 80
 
5.4%

YearsAtCompany
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0054385
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:50.484940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1253325
Coefficient of variation (CV)0.87436818
Kurtosis3.9394498
Mean7.0054385
Median Absolute Deviation (MAD)3
Skewness1.7655031
Sum10305
Variance37.519698
MonotonicityNot monotonic
2025-01-14T12:18:50.740666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 129
8.8%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 129
8.8%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2277362
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:50.991145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6223708
Coefficient of variation (CV)0.85681098
Kurtosis0.47962991
Mean4.2277362
Median Absolute Deviation (MAD)3
Skewness0.91841814
Sum6219
Variance13.12157
MonotonicityNot monotonic
2025-01-14T12:18:51.213020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 373
25.4%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 373
25.4%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

YearsSinceLastPromotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1876275
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:51.449748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2213377
Coefficient of variation (CV)1.4725257
Kurtosis3.6174494
Mean2.1876275
Median Absolute Deviation (MAD)1
Skewness1.9850754
Sum3218
Variance10.377017
MonotonicityNot monotonic
2025-01-14T12:18:51.655690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 160
 
10.9%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 160
 
10.9%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1216859
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-01-14T12:18:51.877458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5673518
Coefficient of variation (CV)0.86550791
Kurtosis0.17312423
Mean4.1216859
Median Absolute Deviation (MAD)3
Skewness0.83450456
Sum6063
Variance12.725999
MonotonicityNot monotonic
2025-01-14T12:18:52.098230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 345
23.5%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 345
23.5%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

Interactions

2025-01-14T12:18:33.319385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:45.038365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:48.225080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:51.981269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:55.940460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:59.245654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:02.629531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:06.247054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:09.364434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:12.619527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:16.146847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:19.420054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:22.707452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:26.412671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:29.987289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:33.535661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:45.232279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:48.433564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:52.183048image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:56.132218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:59.468057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:02.848251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:06.448330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:09.564737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:12.831161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:16.334691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:19.646989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:22.919174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:26.631257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:30.201850image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:33.768673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:45.448159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:48.670124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:52.406115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:56.364657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:59.704781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:03.086790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:06.670427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:09.766623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:13.037797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:16.564516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:19.881496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:23.163668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:26.869267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:30.402148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:34.012327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:45.661304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:48.897905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:52.815833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:56.589979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:59.889132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:03.321055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:06.887388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:10.003345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:13.219166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:16.728398image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:20.102407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:23.363766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:27.102362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:30.580139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:34.421960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:45.814837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:49.098913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:53.007051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:56.789850image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:00.135320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:03.536776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:07.085626image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:10.181078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:13.436753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:16.949534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:20.320074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:23.586379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:27.331102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:30.769899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:34.669479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:46.038350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:49.460225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:53.180996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:57.038538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:00.381704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:03.989689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:07.315329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:10.403273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:13.670197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:17.133772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:20.565481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:23.835871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:27.570931image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:31.003434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:34.914133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:46.264591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:49.692442image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:53.386574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:57.276698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:00.642509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:04.203181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:07.502954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:10.636251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:13.916179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:17.324448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:20.797087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:24.087337image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:27.831205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:31.246511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:35.151197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:46.476112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:49.924338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:53.605927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:57.468514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:00.871079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:04.430610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:07.714682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:10.852434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:14.131844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:17.555295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:21.013099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:24.496256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2025-01-14T12:17:47.532357image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:51.213291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:55.072311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:58.555672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:01.969695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:05.615350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:08.732577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:11.953043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:15.436296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:18.681975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:22.113895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:25.669396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:29.220071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:32.588815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:36.579873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:47.762483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:51.383365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:55.390477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:58.798830image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:02.198404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:05.814530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:08.964784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:12.170678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:15.671604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:18.934792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:22.335912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:25.914266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:29.471643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:32.829164image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:36.734132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:47.981755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:51.698995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:55.703688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:17:59.043706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:02.437543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:06.001123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:09.133198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:12.403626image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:15.902125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:19.169814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:22.530459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:26.153068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:29.725521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T12:18:33.079145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-01-14T12:18:52.382946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2150.0410.0090.000-0.0170.1530.000-0.0010.0000.0000.0280.0300.2960.1760.0000.1400.4740.0160.3510.0000.0060.0000.0340.0930.657-0.0010.0360.2520.1990.1750.195
Attrition0.2151.0000.1230.0620.0730.0620.0000.0860.0000.1190.0160.0440.1380.2190.2310.0990.1710.2170.0220.1120.2430.0000.0000.0320.1980.2080.0760.0890.1760.1700.0280.180
BusinessTravel0.0410.1231.0000.0290.0000.0260.0000.0000.0000.0000.0370.0000.0130.0000.0000.0000.0350.0250.0000.0000.0240.0300.0000.0000.0000.0000.0000.0000.0000.0000.0290.064
DailyRate0.0090.0620.0291.0000.000-0.0020.0180.039-0.0520.0000.0290.0240.0170.0000.0000.0000.0850.016-0.0310.0370.0000.0250.0000.0000.0400.020-0.0110.011-0.0100.007-0.037-0.005
Department0.0000.0730.0000.0001.0000.0000.0000.5880.0340.0180.0260.0000.0000.2130.9370.0290.0300.1880.0000.0320.0000.0000.0000.0180.0000.0230.0060.0470.0000.0000.0000.000
DistanceFromHome-0.0170.0620.026-0.0020.0001.0000.0000.0000.0410.0000.0310.0210.0260.0550.0000.0000.0000.0050.039-0.0110.0620.0300.0590.0260.013-0.001-0.0270.0000.0110.015-0.0040.005
Education0.1530.0000.0000.0180.0000.0001.0000.0550.0450.0210.0000.0000.0000.0870.0510.0160.0000.0940.0380.1000.0070.0200.0000.0160.0270.0950.0280.0000.0700.0300.0000.000
EducationField0.0000.0860.0000.0390.5880.0000.0551.0000.0000.0310.0000.0300.0000.0920.3360.0160.0000.0730.0000.0610.0000.0000.0000.0400.0320.0300.0430.0270.0000.0000.0000.000
EmployeeNumber-0.0010.0000.000-0.0520.0340.0410.0450.0001.0000.0000.0490.0340.0350.0350.0000.0000.0000.0020.0110.0060.017-0.0080.0300.0550.068-0.0030.0260.0000.014-0.0010.007-0.005
EnvironmentSatisfaction0.0000.1190.0000.0000.0180.0000.0210.0310.0001.0000.0000.0000.0340.0000.0000.0000.0190.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0320.0380.0000.000
Gender0.0000.0160.0370.0290.0260.0310.0000.0000.0490.0001.0000.0000.0000.0490.0740.0000.0320.0460.0000.0000.0300.0500.0000.0000.0000.0000.0000.0000.0660.0790.0000.000
HourlyRate0.0280.0440.0000.0240.0000.0210.0000.0300.0340.0000.0001.0000.0000.0000.0230.0070.000-0.020-0.0140.0200.066-0.0100.0000.0000.053-0.0130.0010.000-0.029-0.034-0.052-0.014
JobInvolvement0.0300.1380.0130.0170.0000.0260.0000.0000.0350.0340.0000.0001.0000.0000.0000.0000.0220.0440.0000.0000.0000.0380.0000.0000.0210.0000.0210.0000.0540.0000.0000.043
JobLevel0.2960.2190.0000.0000.2130.0550.0870.0920.0350.0000.0490.0000.0001.0000.5690.0000.0450.8640.0150.1130.0000.0000.0000.0000.0700.5390.0170.0000.3530.2410.2060.232
JobRole0.1760.2310.0000.0000.9370.0000.0510.3360.0000.0000.0740.0230.0000.5691.0000.0000.0600.4230.0000.0790.0000.0000.0000.0300.0390.2930.0000.0310.1880.1320.1110.119
JobSatisfaction0.0000.0990.0000.0000.0290.0000.0160.0160.0000.0000.0000.0070.0000.0000.0001.0000.0000.0000.0460.0000.0240.0000.0260.0000.0000.0240.0230.0000.0000.0000.0000.000
MaritalStatus0.1400.1710.0350.0850.0300.0000.0000.0000.0000.0190.0320.0000.0220.0450.0600.0001.0000.0610.0000.0370.0000.0000.0000.0250.5810.0690.0000.0000.0000.0400.0360.000
MonthlyIncome0.4740.2170.0250.0160.1880.0050.0940.0730.0020.0000.046-0.0200.0440.8640.4230.0000.0611.0000.0540.1900.000-0.0340.0000.0430.0560.710-0.0350.0000.4640.3950.2640.366
MonthlyRate0.0160.0220.000-0.0310.0000.0390.0380.0000.0110.0000.000-0.0140.0000.0150.0000.0460.0000.0541.0000.0210.000-0.0060.0120.0560.0000.012-0.0090.034-0.031-0.007-0.016-0.036
NumCompaniesWorked0.3510.1120.0000.0370.032-0.0110.1000.0610.0060.0000.0000.0200.0000.1130.0790.0000.0370.1900.0211.0000.000-0.0000.0000.0000.0000.314-0.0460.051-0.172-0.128-0.066-0.144
OverTime0.0000.2430.0240.0000.0000.0620.0070.0000.0170.0570.0300.0660.0000.0000.0000.0240.0000.0000.0000.0001.0000.0000.0000.0280.0000.0000.1010.0000.0200.0420.0140.000
PercentSalaryHike0.0060.0000.0300.0250.0000.0300.0200.000-0.0080.0000.050-0.0100.0380.0000.0000.0000.000-0.034-0.006-0.0000.0001.0000.9970.0270.000-0.025-0.0050.000-0.054-0.025-0.056-0.026
PerformanceRating0.0000.0000.0000.0000.0000.0590.0000.0000.0300.0000.0000.0000.0000.0000.0000.0260.0000.0000.0120.0000.0000.9971.0000.0000.0000.0000.0000.0000.0000.0310.0000.030
RelationshipSatisfaction0.0340.0320.0000.0000.0180.0260.0160.0400.0550.0000.0000.0000.0000.0000.0300.0000.0250.0430.0560.0000.0280.0270.0001.0000.0300.0320.0000.0000.0000.0000.0500.000
StockOptionLevel0.0930.1980.0000.0400.0000.0130.0270.0320.0680.0000.0000.0530.0210.0700.0390.0000.5810.0560.0000.0000.0000.0000.0000.0301.0000.0640.0000.0180.0140.0230.0560.030
TotalWorkingYears0.6570.2080.0000.0200.023-0.0010.0950.030-0.0030.0000.000-0.0130.0000.5390.2930.0240.0690.7100.0120.3140.000-0.0250.0000.0320.0641.000-0.0150.0000.5940.4930.3340.495
TrainingTimesLastYear-0.0010.0760.000-0.0110.006-0.0270.0280.0430.0260.0000.0000.0010.0210.0170.0000.0230.000-0.035-0.009-0.0460.101-0.0050.0000.0000.000-0.0151.0000.0000.0010.0040.011-0.012
WorkLifeBalance0.0360.0890.0000.0110.0470.0000.0000.0270.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0340.0510.0000.0000.0000.0000.0180.0000.0001.0000.0190.0250.0000.031
YearsAtCompany0.2520.1760.000-0.0100.0000.0110.0700.0000.0140.0320.066-0.0290.0540.3530.1880.0000.0000.464-0.031-0.1720.020-0.0540.0000.0000.0140.5940.0010.0191.0000.8540.5190.843
YearsInCurrentRole0.1990.1700.0000.0070.0000.0150.0300.000-0.0010.0380.079-0.0340.0000.2410.1320.0000.0400.395-0.007-0.1280.042-0.0250.0310.0000.0230.4930.0040.0250.8541.0000.5050.725
YearsSinceLastPromotion0.1750.0280.029-0.0370.000-0.0040.0000.0000.0070.0000.000-0.0520.0000.2060.1110.0000.0360.264-0.016-0.0660.014-0.0560.0000.0500.0560.3340.0110.0000.5190.5051.0000.466
YearsWithCurrManager0.1950.1800.064-0.0050.0000.0050.0000.000-0.0050.0000.000-0.0140.0430.2320.1190.0000.0000.366-0.036-0.1440.000-0.0260.0300.0000.0300.495-0.0120.0310.8430.7250.4661.000

Missing values

2025-01-14T12:18:37.172819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-14T12:18:38.210426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-14T12:18:38.725965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041.0NaNTravel_Rarely1102Sales1.02Life Sciences112Female9432Sales Executive4Single5993194798YYes11318008016405
149.0NoTravel_Frequently279Research & Development8.01Life Sciences123Male6122Research Scientist2Married5130249071YNo2344801103310717
237.0YesTravel_Rarely1373Research & Development2.02Other144Male9221Laboratory Technician3Single209023966YYes15328007330000
333.0NoTravel_Frequently1392Research & Development3.04Life Sciences154Female5631Research Scientist3Married2909231591YYes11338008338730
427.0NoTravel_Rarely591Research & Development2.01Medical171Male4031Laboratory Technician2Married3468166329YNo12348016332222
532.0NoTravel_Frequently1005Research & Development2.02Life Sciences184Male7931Laboratory Technician4Single3068118640YNo13338008227736
659.0NoTravel_Rarely1324Research & Development3.03Medical1103Female8141Laboratory Technician1Married267099644YYes204180312321000
730.0NoTravel_Rarely1358Research & Development24.01Life Sciences1114Male6731Laboratory Technician3Divorced2693133351YNo22428011231000
838.0NoTravel_Frequently216Research & Development23.03NaN1124Male4423Manufacturing Director3Single952687870YNo214280010239718
936.0NoTravel_Rarely1299NaN27.03Medical1133Male9432Healthcare Representative3Married5237165776YNo133280217327777
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
146129.0NoTravel_Rarely468Research & Development28.04Medical120544Female7321Research Scientist1Single378584891YNo14328005315404
146250.0YesTravel_Rarely410Sales28.03Marketing120554Male3923Sales Executive1Divorced10854165864YYes133280120333220
146339.0NoTravel_Rarely722Sales24.01Marketing120562Female6024Sales Executive4Married1203188280YNo1131801212220996
146431.0NoNon-Travel325Research & Development5.03Medical120572Male7432Manufacturing Director1Single993637870YNo193280010239417
146526.0NoTravel_Rarely1167Sales5.03Other120604Female3021Sales Representative3Single2966213780YNo18348005234200
146636.0NoTravel_Frequently884Research & Development23.02Medical120613Male4142Laboratory Technician4Married2571122904YNo173380117335203
146739.0NoTravel_Rarely613Research & Development6.01Medical120624Male4223Healthcare Representative1Married9991214574YNo15318019537717
146827.0NoTravel_Rarely155Research & Development4.03Life Sciences120642Male8742Manufacturing Director2Married614251741YYes20428016036203
146949.0NoTravel_Frequently1023Sales2.03Medical120654Male6322Sales Executive2Married5390132432YNo143480017329608
147034.0NoTravel_Rarely628Research & Development8.03Medical120682Male8242Laboratory Technician3Married4404102282YNo12318006344312

Duplicate rows

Most frequently occurring

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager# duplicates
028.0YesTravel_Rarely1157Research & Development2.04Medical14401Male8411Research Scientist4Married3464247375YYes133480054232222